Prompt-based Pre-trained Model for Personality and Interpersonal Reactivity Prediction

• URL: http://arxiv.org/abs/2203.12481v1
• Date: Wed, 23 Mar 2022 15:22:34 GMT
• Title: Prompt-based Pre-trained Model for Personality and Interpersonal Reactivity Prediction
• Authors: Bin Li, Yixuan Weng, Qiya Song, Fuyan Ma, Bin Sun, Shutao Li
• Abstract summary: This paper describes the LingJing team's method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI)
• Score: 19.288384399961867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes the LingJing team's method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI). In this paper, we adopt the prompt-based method with the pre-trained language model to accomplish these tasks. Specifically, the prompt is designed to provide the extra knowledge for enhancing the pre-trained model. Data augmentation and model ensemble are adopted for obtaining better results. Extensive experiments are performed, which shows the effectiveness of the proposed method. On the final submission, our system achieves a Pearson Correlation Coefficient of 0.2301 and 0.2546 on Track 3 and Track 4 respectively. We ranked Top-1 on both sub-tasks.

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